Jülicher Vortragsabend zum Jahresabschluss 2021 | Jung. Forscht. Wirkt.

Malaria, one of the oldest known infectious diseases on Earth, is still present in tropical and developing countries with a high statistical incidence. Malaria is caused by the bite of an Anopheles mosquito, which transmits the Plasmodium (P.) parasites. Despite the efforts to diagnose and treat malaria correctly, challenges remain in ensuring a sensitive, selective, and discriminatory detection of P. parasites. Current commercial malaria rapid diagnostic tests can lead to false positives and incorrect treatments.

Gabriela Figueroa Miranda and Viviana Rincón Montes have developed a low-cost and multi-target malaria biosensor. The biosensor is a user-friendly diagnostic test, which comprises a disposable chip sensor, soaked with a blood drop sample, and a re-usable portable reader. This new biosensor allows the selective and sensitive detection of malaria, making it possible to quantify the parasite load of the infection and a distinction to be made between the two main malaria parasites. As part of their start-up project, this new system is being established as a technology platform for the future development of new biosensors to detect and discriminate between novel and emerging diseases, such as COVID-19 or the West-Nile fever.

This year’s extreme weather events have emphasized once again the importance of improving our understanding of both climate and weather. Careful and detailed observations are key to this endeavour. The resulting datasets must be stored in digital form before the sophisticated task of interpreting them can be performed.

Obtaining such datasets can be very tricky and interesting: Johannes Laube collects air samples from all over the world to analyse their content of various trace gases that are important for global warming or the life-protecting ozone layer. A special challenge is the collection of air from the stratosphere about 10 to 50 km above ground. Recent advances in balloon-borne sensors are opening new possibilities here. Scarlet Stadtler’s interdisciplinary approach involves developing AI techniques to identify patterns within the data. She then applies the trained AI models to unknown spaces. AI has the potential to take predictions of the global climate and weather system to the next level.

Understanding the human brain is a key challenge to understand aging pathologies and mental diseases. Thanks to advances in data acquisition technology, especially magnetic resonance imaging, we can now observe how the brain functions non-invasively and in vivo. Sarah Genon uses data from neuroimaging scanners, psychological tests, and lifestyle surveys to study how the brain is organized and how this organization relates to behavioural functions and dysfunction.

For this reason, they gather together very large datasets of on thousands of participants from different populations in the world. Kaustubh Patil combines them with machine learning methods and modern computing infrastructure. This allows the building of models that can predict behaviour or disease status at an individual level, an essential step towards precision medicine. The insights provided by such models help to learn about the brain regions and their interactions responsible for a behaviour or dysfunction due to a disease.